This study aimed at assessing the correctness of a caregiver’s perception of their child’s diet status and to determine the factors which may influence their judgment. 815 child-caregiver pairs were recruited from two primary schools. 3-day 24-h recall was used to evaluate children’s dietary intake, Chinese Children Dietary Index (CCDI) was used to evaluate the dietary quality. Multivariate logistic regression models were used to explore the factors that could influence the correctness of caregiver’s perception. In the current study, 371 (62.1%) children with “high diet quality” and 35 (16.1%) children with “poor diet quality” were correctly perceived by their caregivers. Children who were correctly perceived as having “poor diet quality” consumed less fruits and more snacks and beverages than those who were not correctly perceived (p < 0.05). Obese children were more likely to be correctly identified as having “poor diet quality” (OR = 3.532, p = 0.040), and less likely to be perceived as having “high diet quality”, even when they had a balanced diet (OR = 0.318, p = 0.020). Caregivers with a high level of education were more likely to correctly perceive children’s diet quality (OR = 3.532, p = 0.042). Caregivers in this study were shown to lack the ability to correctly identify their children’s diet quality, especially amongst children with a “poor diet quality”. Obesity, significantly low consumption of fruits or high consumption of snacks can raise caregivers’ awareness of “poor diet quality”.

This study aimed to understand the consumption frequency of sugar-sweetened beverages (SSBs) and high-energy diets in junior school students in China and to explore the relationship between SSBs and high-energy diets and academic performance. Information about 9251 junior school students was retrieved from the China Education Panel Survey (CEPS) database. The Mann–Whitney U test and the Kruskal–Wallis test were used to compare differences in academic performance based on the variables of interest. Generalized linear mixed models were used to analyze the association between the consumption frequency of SSBs and high-energy diet and student academic performance, fixed and random effects were included to control for confounding factors. The proportions of the “often” consumption group of SSBs and high-energy diets were 21.5% and 14.6%, respectively. For SSBs, the total score of the “often” consume group was 4.902 (95%CI: −7.660~−2.144, p < 0.001) points lower than that of the “seldom” consume group. Scores of Chinese math, and English were 0.864 (95%CI: −1.551~−0.177, p = 0.014), 2.164 (95%CI: −3.498~−0.831, p = 0.001), and 1.836 (95%CI: −2.961~−0.710, p = 0.001) points lower, respectively. For high-energy diets, the scores of total Chinese and English in the “sometimes” consume group were 2.519 (95%CI: 0.452~4.585, p = 0.017), 1.025 (95%CI: 0.510~1.540, p < 0.001) and 1.010 (95%CI: 0.167~1.853, p = 0.019) points higher than that of the “seldom” consume group, respectively. Our findings suggested that consumption of SSBs was often negatively associated with academic performance in junior school students, while medium consumption of high-energy diets had a positive correlation. The positive association between high-energy diets and academic performance may be related to the food items included in the high-energy diets consumed by Chinese students. Schools and families should pay more effort to reduce the consumption of SSBs, and for high-energy diets, the focus should be on food selection and avoiding excessive intake. Longitudinal studies are needed to further test these findings among adolescents.

To cope with the challenges of monitoring dynamic and variable quality variation into supply chain, diagnosing the abnormal variation at the right moment, is a difficult problem that a enterprise in supply chain faces in process quality control. A dynamic process quality control method, which integrated quality prevention, analysis and diagnosis, was all put forward. This method integrated several enabling technologies such as the theory of similarity manufacturing, Statistical Process Control (SPC), neural network. Furthermore, some key enabling technologies were studied in detail, including process quality analysis on-line based on similarity process and process quality diagnosis based on Elman. It is basis of realizing network, intelligent and automatic process quality control.